The following document is hereby incorporated by reference into this specification: Bulsara, A. R. and G. Schmera, May 1993, "Stochastic Resonance in Globally Coupled Nonlinear Oscillators", Physical Review E, Vol. 47, no. 5, pp. 3734-3737.
This invention pertains broadly to the field of signal processing. More particularly, the invention pertains to a signal processor that exploits noise to amplify a signal of interest. In greater particularity, but without limitation thereto, the signal processor of the invention utilizes the phenomenon of stochastic resonance in a nonlinear dynamic system to transfer power from a noise background to a signal of interest.
Traditional signal processing has relied on various combinations of linear filters, including numerical techniques such as the Fast Fourier Transform (FFT), that are realizable in both hardware and software. Though the FFT is applicable to signals of any frequency, its use requires significant computation.
Hardware filters or processors for very low frequencies can be difficult to design. Such filters are, typically, tuned inductor-capacitor resistor (LCR) circuits, the resonant frequency of which is changed by capacitor and inductor adjustment. For very low frequencies, practical limitations exist on the magnitudes of the circuit inductance and capacitance one can realize while still producing a high quality circuit.
Software filters have been developed to overcome the deficiencies of many analog filters, but implementing software filters can require complex hardware and have significant computational requirements.
In the above-described conventional signal processing methods, noise, whether created naturally or intentionally, is usually considered a disruption or a hindrance to communication. This noise is usually eliminated or substantially reduced through filtering. In fact, ever since the advent of telephone and radio, engineers have devoted tremendous efforts to eliminating or minimizing the effects of noise. As a result, an entire discipline known as linear filter theory has evolved and has become standard teaching to electrical engineering and/or communication students.
In the cognitive and neural science areas, a nonlinear filtering process known as stochastic resonance (SR) has been investigated. To those schooled in linear doctrine, filtering with SR begins with a radical premise: that noise, either inherent or generated externally, can be used to enhance the flow of information through certain nonlinear systems.
Stochastic resonance is a nonlinear stochastic phenomenon which can effectively cause a transfer of energy from a random process (noise) to a periodic signal over a certain range of signal and system parameters. It has been observed in natural and physical systems and may be one means by which biological sensor systems amplify weak sensory signals for detection.
Stochastic resonance has actually been demonstrated in a variety of physical experiments ranging from ring lasers to a number of solid state devices including SQUIDs (super-conducting quantum interference devices) and tunnel diodes.
Peter Jung, Ulrich Behn, Eleni Pantazelou and Frank Moss have proposed that in a network consisting of an infinite number of linearly coupled bistable oscillators with linear mean-field interaction, the stochastic resonance effect is enhanced over what would be expected for a single isolated element of the network. In examining output signal only, the theory proposed is confined to the bifurcation point of the effective bistable potential that describes the network dynamics (in the large N limit) and appears inapplicable away from the bifuraction critical point. In this linear coupling theory, the strength and signs of all coupling coefficients must be the same.